Typical machine learning involves a large number of computations that consume a large number of computational resources. When machine learning is applied to monitor large scale networks, immense amounts of computational resources are utilized. Specifically, as cheaper and more attainable computing nodes, e.g. IoT devices, are more widely adopted, networks continue to grow in size and complexity. In turn, this makes utilizing machine learning to monitor computing nodes in such networks difficult as a result of the large number of computational resources needed to actually monitor the computing nodes using machine learning.
Further, machine learning is typically applied to monitor networks from a centralized location or a centralized computational resource group, e.g. the cloud. As discussed previously, monitoring complex networks through machine learning requires large numbers of computational resources, making centralized implementation of network monitoring using machine learning extremely challenging. For example, the overhead of moving large amounts of data needed to monitor networks centrally using machine learning is vast making such implementation infeasible. There therefore exist needs for systems and methods of distributing computational resources for performing network monitoring using machine learning away from a central region. Specifically, there exist needs for distributing network monitoring through machine learning to computational resources at the edges of a network.
Additionally, telemetry data from computing nodes is typically used to monitor networks using machine learning. This is problematic when the networks are monitored from a centralized location. In particular, the telemetry data is sent from the computing nodes away from the edge of the network, potentially exposing the telemetry data. This presents security concerns that often preclude device owners from sharing telemetry data with outside parties. In turn, this makes applying machine learning to monitor networks problematic, as often times monitoring networks using machine learning is accomplished with telemetry data describing device behavior that is generated at or near the devices. There therefore exist needs for systems and methods of sending data describing computing node behavior away from a network edge, e.g. a LAN, without actually exposing telemetry data of the computing nodes.